Retail Customer Churn Prediction: How-To Guide Now Available

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Predicting customer churn rate is among the most sought-after machine learning and analytics applications for retail stores, and of high value to companies that are eager to take advantage of the ever-increasing amounts of customer data they are collecting. Retaining existing customers is estimated to be five times cheaper than the cost of attracting new ones, and so businesses want to be proactive about things and predict who is likely to churn before it happens. Businesses also wish to identify the factors that are related to high churn rates, which in turn helps them apply resources towards acquiring the right type of customers in the first place.

Microsoft has been active in the domain of churn prediction, having published several resources to help businesses understand the data science process behind customer churn prediction.

The specific business case in the Guide is about predicting churn rate such that the question "What is the probability that a customer will churn soon?" can be answered.

We say a customer churned when that customer spent no money at the store in the last 21 days. This definition can be customized by two factors: the number of days from today and the amount of money spent. For example, some businesses might define a churned customer as someone who has made less than $10 in purchases over the last 30 days. The problem is formatted as a two-class classification problem, and a machine learning algorithm is used to create the predictive model that learns from the simulated data based on the Tafeng dataset, which can be found in this GitHub repository resource folder. The data includes transaction-level information such as user-id, item-id, quantity, and value, as well as user-level information such as age and region.

Who will Benefit from the Guide?

The Guide was developed with three distinct audiences in mind: business decision makers, data scientists, and engineers.

Data scientists and engineers will benefit from the Technical Deployment Guide, which provides detailed instructions on how to stitch together on-premises and Azure services. The Technical Deployment Guide includes an Azure ML experiment that provides a starting point for data scientists to develop churn prediction models. Interested data scientists can also learn to generate powerful visualizations using Power BI.